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36 tools reviewed

AI Agent Infrastructure Tools

Agent infrastructure tools help teams build AI systems that can use tools, browse, remember context, evaluate outputs, and run workflows. They require stronger engineering controls than simple chat assistants.

Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.

How to choose in this category

Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.

Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.

Check sandboxing, approvals, audit trails, and failure modes.

Review supported models and integration surfaces.

Related category guides

Agent Infrastructure tools

Compare official links, pricing notes, business fit, and alternatives for each tool.

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Orgo logo

Orgo

Free plan + paid plans

Persistent cloud desktops for AI agents, with snapshots and one-click cloning

Best for

Startups building computer-use agents on Claude/OpenAI CUA, Agencies giving each client a forkable workspace

Browser Use logo

Browser Use

Free plan + paid plans

Cloud browser plus agent harness that lets AI sign up, log in, and act on any website

Best for

Agent startups doing self-registration and authenticated workflows, Enterprise RPA teams replacing UiPath on web-only tasks

Browserbase logo

Browserbase

Free plan + paid plans

Managed headless browsers, search, and fetch APIs for AI agents at scale

Best for

Series A/B agent companies needing battle-tested browser infra, Enterprises with HIPAA, SOC2, or SSO requirements

Hyperbrowser logo

Hyperbrowser

Published pricing

Cloud browser infrastructure for AI agents

Best for

Devs comparison-shopping against Browserbase and Steel, Startups wanting a cheaper cloud-browser option

Steel logo

Steel

Free plan + paid plans

Open-source cloud browser API with self-host option for AI agents and scrapers

Best for

Indie devs and scrappy startups on tight infra budgets, Teams that insist on an open-source escape hatch

Anchor Browser logo

Anchor Browser

Free plan + paid plans

Cloud browser agents tuned for deterministic, low-token automation with built-in auth and VPN

Best for

SaaS PMs building competitive integrations fast, BPO and services firms cutting manual ops cost

Scrapybara logo

Scrapybara

Free plan + paid plans

Virtual Ubuntu and Windows desktops for OpenAI CUA and Claude computer-use agents

Best for

Devs building on OpenAI's Computer Use Agent, Anthropic computer-use early adopters

E2B logo

E2B

Free plan + paid plans

Secure microVM sandboxes for AI agents to run code

Best for

Devs shipping ChatGPT-style code interpreter features, AI startups building autonomous coding agents

Composio logo

Composio

Free plan + paid plans

Authenticated tool-calling for agents across 1,000+ apps

Best for

Agent framework users (LangChain, CrewAI, custom) wanting plug-in tools, Startups shipping multi-app agent features fast

Smithery logo

Smithery

Published pricing

Marketplace and managed hosting for MCP servers

Best for

MCP-first developers and indie agent builders, Teams publishing MCP servers for others to consume

Arcade logo

Arcade

Free plan + paid plans

Per-user OAuth and MCP runtime for production agents

Best for

B2B SaaS teams adding agent features with end-user auth, Platform teams standardizing OAuth for many internal agents

Mastra logo

Mastra

Free plan + paid plans

TypeScript-first agent framework with hosted observability

Best for

TypeScript developers building production agents, Next.js/Express teams adding agent features to existing apps

Letta logo

Letta

Free plan + paid plans

Stateful agents with memory you own and port across models

Best for

Developers wanting a memory-first alternative to Claude Code/Cursor, AI researchers experimenting with stateful agents

Mem0 logo

Mem0

Free plan + paid plans

Drop-in memory API that shrinks prompts and remembers users

Best for

Devs adding user memory to a chatbot without building a RAG stack, Healthcare/education/CS teams needing audited memory storage

Zep logo

Zep

Free plan + paid plans

Temporal knowledge graph for agent memory and context

Best for

Engineering leaders shipping personalization without a dedicated ML team, Devs frustrated with vector-only RAG for stateful agents

Cognee logo

Cognee

Free plan + paid plans

Knowledge graph memory layer for AI agents, with 28+ source connectors built in

Best for

Solo developers prototyping agents with MCP, Platform teams unifying scattered data sources for agents

Supermemory logo

Supermemory

Free plan + paid plans

Hosted memory API for AI agents, with native connectors and rich-content ingestion

Best for

Indie devs and small teams building consumer AI products, Startups that need user-data connectors fast

Pinecone logo

Pinecone

Free plan + paid plans

Serverless managed vector database, the default pick for production RAG at scale

Best for

Engineering teams that want managed vector search with no infra work, Companies with billion-scale embedding workloads

Qdrant logo

Qdrant

Free plan + paid plans

Open-source Rust vector DB with hybrid search and the strongest filtering story

Best for

Teams that prefer open-source with optional managed, Workloads with heavy metadata filtering

Weaviate logo

Weaviate

Free plan + paid plans

Vector database with built-in agent and query primitives, cloud or self-hosted

Best for

AI engineers who want a batteries-included vector platform, Teams using GraphQL elsewhere in the stack

Chroma logo

Chroma

Free plan + paid plans

Object-storage-native vector DB, the cheapest at-rest economics in the category

Best for

Developers already using open-source Chroma in production, Cost-sensitive teams with large but cold vector datasets

Milvus logo

Milvus

Free plan + paid plans

Open-source vector DB built for billion-scale workloads, with GPU index support

Best for

Platform teams operating large-scale ML infrastructure, Companies with on-prem or air-gapped requirements

LanceDB logo

LanceDB

Contact sales

Multimodal lakehouse for AI training data, replaces five tools with one columnar table

Best for

ML platform teams at AI-first companies, Foundation model and generative AI labs

Tavily logo

Tavily

Free plan + paid plans

Search and extraction API that grounds AI agents in live web data with safety filters.

Best for

LLM app developers adding live web context to RAG, Enterprise AI teams needing injection-filtered search

Exa logo

Exa

Usage-based

Agent-native search API with deep-research mode and token-efficient content highlights.

Best for

Teams building autonomous research agents, B2B sales platforms needing people and company enrichment

Firecrawl logo

Firecrawl

Free plan + paid plans

Scrape, crawl, and interact with any site, returning LLM-ready markdown and JSON.

Best for

AI developers building RAG pipelines with web sources, Sales and growth teams running enrichment workflows

AgentOps logo

AgentOps

Contact sales

Trace, debug, and deploy AI agents with session replay and cross-framework cost tracking.

Best for

Engineering teams running CrewAI, Autogen, or LangChain agents, Enterprises needing on-prem agent observability

Galileo logo

Galileo

Free plan + paid plans

Eval-to-guardrail platform with low-cost Luna judge models for production AI monitoring.

Best for

Enterprise AI teams deploying agents at production scale, Companies needing runtime guardrails, not just batch evals

Traceloop logo

Traceloop

Free plan + paid plans

OpenTelemetry-native LLM monitoring and evals, one line of code to instrument.

Best for

Engineering teams already using OpenTelemetry, Open-source-first companies wary of proprietary agents

Patronus AI logo

Patronus AI

Usage-based

Simulation environments and evaluator APIs for training and testing frontier AI agents.

Best for

AI labs training or fine-tuning frontier models, Financial-services AI teams needing domain benchmarks

Giskard logo

Giskard

Free plan + paid plans

Automated red-teaming and hallucination testing for LLM agents, with dashboards for non-coders.

Best for

Enterprise AI teams in regulated industries, Security teams red-teaming LLM applications

Ragas logo

Ragas

Contact sales

Open-source eval framework purpose-built for RAG pipelines

Best for

ML engineers building production RAG on LangChain/LlamaIndex, Applied research teams iterating on retrieval quality

DeepEval logo

DeepEval

Free plan + paid plans

Pytest-native LLM evals with 50+ metrics, runs locally in your editor

Best for

Backend/ML engineers shipping LLM features behind tests, Teams standardizing on pytest for AI quality gates

Confident AI logo

Confident AI

Free plan + paid plans

Hosted eval + observability + red-teaming layer on top of DeepEval

Best for

Regulated enterprises needing SOC 2/HIPAA eval governance, Platform teams enforcing one eval standard org-wide

Trigger.dev logo

Trigger.dev

Free plan + paid plans

Durable TypeScript background jobs and AI agents with full runtime control

Best for

TypeScript/Next.js teams building AI agents, Startups avoiding self-managed worker fleets

Inngest logo

Inngest

Usage-based

Multi-language durable workflows and AI agents that run on your existing infra

Best for

Polyglot engineering teams (Python + TS + Go), High-volume event-driven products needing >100k/sec

Common questions about Agent Infrastructure

What are Agent Infrastructure tools used for?

Agent infrastructure tools help teams build AI systems that can use tools, browse, remember context, evaluate outputs, and run workflows. They require stronger engineering controls than simple chat assistants.

Which Agent Infrastructure tools should a business compare first?

Start by reviewing Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, then compare pricing, implementation effort, integrations, and workflow ownership against your actual use case.

How should buyers choose between Agent Infrastructure vendors?

Use criteria such as Compare tool-calling, memory, browser automation, evals, observability, and deployment controls; Check sandboxing, approvals, audit trails, and failure modes; Review supported models and integration surfaces.